Tutorial D

Tutorial D	Demystifying Machine Learning and Artificial Intelligence: How AI/ML and Generative AI will Revolutionize the Aerospace Industry
Fees	General Tutorial Attendee (Half-Day): $250 Full-Time Student (Half-Day): $125
Date	Monday, February 23, 2026
Time	8:00 AM – 11:30 AM PT
Overview	Artificial Intelligence (AI) has been proposed as a solution to a myriad of challenges analysts face when evaluating and determining courses of action based on information derived from data. Despite staggering innovation and investment into AI in the private and public sector, misinformation surrounding AI and its potential to solve difficult problems persists. This short course builds upon previous courses by providing technical fundamentals of AI, focused on Machine Learning (ML), Neural Networks, and other innovative advances, such as generative AI. The course explores how these tools and capabilities can be leveraged as potential solutions to problems of interest to GSAW participants. This course presents a technical overview of current approaches centered around crucial algorithms for aspiring or current users so they can successfully utilize ML techniques in their data exploitation. A key aspect of this course is the discussion of how and when ML may or may not be appropriate for use. Of great interest to the communities that are considering using ML is understanding of the information the models provide as well as how to build trust in the models and algorithms and how to ensure that the results are both correct and unbiased. These will be covered in detail so that participants can use ML to solve problems and have confidence in the results. Course Outline: What is Machine Learning, Deep Learning. Artificial Intelligence, and Generative AI Types of Machine Learning Model definition\Data feature definition and extraction Machine Learning Fundamentals Unsupervised Learning Supervised Learning Validation of model accuracy Pros and cons Deep Learning and Neural Networks Neural networks Components Layers Activation functions Optimization Convolutional Neural Networks Architectures Pros and cons Generative AI Large language models ChatGPT and similar models Trust, fakes and detecting issues in data Ethical AI Explainable AI and building trust Data biases and how to avoid them Advances in Machine Learning
Instructors	Joseph Coughlin and Lauren Perry, The Aerospace Corporation
Biographies	Joseph Coughlin is an Associate Director at Aerospace Corporation working on projects to improve the utilization of sensors and their data for Space Domain Awareness (SDA) application and working for the USSF and SpOC Chief Data Offices to define data usage and standards. He has been instrumental in bringing operational analytics and machine learning technologies to data analysis for SDA missions. He received a Master’s in Astrophysical, Planetary and Atmospheric Physics from the University of Colorado. Lauren Perry is a Principal Engineer within the Information Systems and Cyber Division (ISCD) at The Aerospace Corporation. She is currently the deputy AI-Integration lead for The Aerospace Corporation, defining, acquiring and developing Enterprise AI capabilities to support Aerospace work and customers. She has played a pivotal role in developing Aerospace’s Trusted AI Framework and leading the Mission Assurance for AI corporate focus area. With a background in supporting large-scale software acquisition programs and a dedication to enterprise integration, Ms. Perry has earned multiple awards for her technical insights and mission success. She joined Aerospace in 2017, bringing experience from Systems Planning and Analysis, Inc. and Lockheed Martin Space Systems Company. Ms. Perry holds a Bachelor’s in Mathematics and Statistics from James Madison University and a Master’s in Applied Statistics from Pennsylvania State University.
Description of Intended Audience and Recommended Prerequisites	Target Audience: Tutorial is designed for a non-technical as well as a technical audience. Tutorial is for those interested in learning more about different aspects of Machine Learning and Artificial intelligence, especially as it can apply to ground system and satellite applications. Students should have a desire to learn the details of how Artificial Intelligence can be implemented for data exploitation and the benefits and pitfalls of the different approaches. This year there will be an added emphasis on Generative AI, how to ensure trust in the models through explainable AI as well as new topics such as ethical AI and the strengths and limitations of large language models such as ChatGPT. Prerequisites: No prerequisites are needed.
What can Attendees Expect to Learn	What Machine Learning, Artificial Intelligence, and generative AI can provide versus the hype. How to use Machine Learning and AI for Ground and Space Systems and when not to use it. How to properly identify data features for correctly building models Fundamentals on Machine Learning algorithms, such as supervised and unsupervised learning and neural networks, to enable students to understand the benefits for using one approach versus another. • What is Deep Learning and the different types of Neural Networks and their components. Ethical AI and biases in AI applications and how to consider them in your model. Explainable AI tools and processes and why they are important for building trusted ML system. Generative AI, such as ChatGPT, and its ramifications for use in government systems.

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